BEST-COST GitHub repository

Let’s start by checking out the BEST-COST GitHub repo and the README file

Landing page of the BEST-COST GitHub repo. The README file tells you how to get started. The folder r_package contains package-related files (including function code). Developments are discussed and documented under Issues.

Landing page of the BEST-COST GitHub repo. The README file tells you how to get started. The folder r_package contains package-related files (including function code). Developments are discussed and documented under Issues.

README

The README file contains the following information

README file of the healthiar R package

About healthiar (1/2)

Figure: healthiar overview ## About healthiar (2/2)

The core function family of healthiar is the attribute family, used to attribute health impacts to a (environmental) risk factor, e.g. noise or air pollution. - attribute_health can be used for relative and absolute risk assessments - attribute_lifetable is used for (RR & AR) life table assessment

The extended family is the compare family, which is used to compare two scenarios with the population impact fraction (PIF) or delta approach.

Then there are the in-laws, which are the “additional” analysis functions that can be run after the initial attribute or compare function calls - Economic analysis (WP2) + monetize + cba - Social analysis (WP3) + get_mdi + socialize - Summary uncertainty (i.e. Monte Carlo simulation) - Multi-pollutant exposure + INSERT_NAME TODO

healthiar package environment in RStudio

healthiar in RStudio 1/3

RStudio startup screen

healthiar in RStudio 2/3

Post installation, you can access the healthiar package landing page in RStudio by going to the Packages tab and then clicking on the healthiar package.

healthiar in RStudio 3/3

Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

Vignette

The vignette introduces healthiar step-by-step and contains (reproducible) examples. You can open the intro_to_healthiar vignette within RStudio or as a HTML within your browser.

Note

The vignette is a work in progress: we appreciate any feedback or suggestions you might have to make it more useful to future users!

Function documentation (1/) - Overview

Access the function documentation (= fun doc) by clicking on a function name in the package landing page.

Tip

When the package is loaded (via library(healthiar)) access the fun doc of e.g. attribute_health by running ?attribute_health.

Any fun doc contains the following sections:

  • Title Essence of the function

  • Description What does the function do exactly?

  • Usage Bare-minimum examples of how to use the function (includes default values if there are any)

  • Arguments Short description of each function argument: input type (numeric vs. string), options (if available), how each argument affects the output.

  • Details (optional) Additional details about the function

  • Value Information about the function output

  • Examples (optional) Shows how the function works

Function documentation (1/) - Title

Title section

The title summarizes the function of the function (hehe) in one sentence.

The title shows up next to the function name in the package landing page.

Function documentation (1/) - Description

Description section provides additional details about the function’s purpose

Function documentation (1/) - Usage

Usage section In the usage section you can find a bare-minimum function “template”, which can either be auto-generated or created manually, as in this case.

Important

Any arguments that appear without a = symbol after them in the usage section have to be user-specified in all function call.

Note

The inputs to the arguments in the usage section are default inputs

Function documentation (1/) - Arguments

Arguments section This is the core section of the function documentation, where input type (numeric vs. string) & input options (if available) are specified.

Function documentation (1/) - Details

Details section (optional) Additional details about the function ::: callout-warning Depending on the function, this section might be not very developed at the moment. Sometimes more function details are found in the intro vignette. :::

Function documentation (1/) - Value

Value section Information about the function output

Function documentation (1/) - Examples

Example section (optional) Shows how the function works

Tip

By clicking on Run examples the example(s) are executed and the output shown

Function documentation (1/) - Examples output

Example output Obtained by clicking on Run examples (see previous slide)

Workflow BoD

note: quickly introduce the main workflow of burden assessments

Example: attribute call without input uncertainties

Goal: attribute COPD cases to air pollution

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear", # Alternatives: "linear", "log_log"
    rr_central = 1.369, 
    rr_increment = 10,  # μg / m^3
    exp_central = 8.85, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 30747 # baseline health data: COPD incidence
  ) 

Tip

healthiar comes with some example data that start with exdat_ that allow you to test functions.

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = exdat_pm_copd$relative_risk, 
    rr_lower = exdat_pm_copd$relative_risk_lower,
    rr_upper = exdat_pm_copd$relative_risk_upper,
    rr_increment = 10, 
    exp_central = exdat_pm_copd$mean_concentration,
    cutoff_central = exdat_pm_copd$cut_off_value,
    bhd_central = exdat_pm_copd$incidents_per_100_000_per_year/1E5*exdat_pm_copd$population_at_risk,
    # bhd_central = exdat_pm_copd$incidence # Uncomment once change committed to main
  ) 

Output structure

Every attribute output consists of two lists (“folders”)

  • health_main contains the main results

  • health_detailed detailed results (and in some cases even more information about the assessment/calculation)

note: add here that format is tibble (but can be changed to data frame)

note: screenshot of how to click on variable in env (put that as first option) . . .

Different ways to access the results

Tip

This is personal preference! However, you might encounter all options.

Go to the Environment tab in RStudio and click on a variable to “open” it. Alternatively, you can use View(results_pm_copd), which has the same effect.

results_pm_copd$health_main$impact_rounded

Note: after typing the $ sign you can see all available options by pressing the tab key and use the arrows & tab keys to select an option (or alternatively use the mouse)

results_pm_copd[["health_main"]]

Note: if the cursor is located within the square braces you can see all available options by pressing the tab key

Using the purrr::pluck function to select a list and then the dplyr::pull function extract values from a specified column

results_pm_copd |> purrr::pluck("health_main") |> dplyr::pull("impact_rounded")

Note: available options can’t be displayed automatically using these functions -> better suited for a more permanent analysis script

Let’s check the main output!

results_pm_copd[["health_main"]]
impact_rounded impact pop_fraction erf_ci rr exp bhd
3502 3501.962 0.1138961 central 1.369 8.85 30747
1353 1353.066 0.0440064 lower 1.124 8.85 30747
5474 5473.888 0.1780300 upper 1.664 8.85 30747

Tip

Each row shows a result obtained with all the input data & calculation pathway specifications shown in that row

Some of the most relevant columns include: - impact_rounded Rounded attributable health impact/burden - impact Raw impact/burden - pop_fraction Population attributable fraction (PAF) - erf_ci Specifies whether rr_central, ..._lower or ..._upper was used in calculation - rr Specifies raw rr used in calculation - - exp - bhd*

Example: attribute with input uncertainties

Goal: attribute lung cancer deaths to PM2.5 exposure

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, 
    rr_upper = 1.664,
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000
  ) 

Let’s check the detailed output!

Tip

See the intro vignette for a detailed description of output columns.

The health_detailed output table contains all different combinations of the arguments with uncertainty. E.g. rr_central with exp_lower and bhd_upper, …

results_pm_copd[["health_detailed"]][["raw"]]
geo_id_disaggregated erf_ci exp_ci bhd_ci cutoff_ci pop_fraction impact prop_pop_exp rr_increment erf_shape exposure_name approach_risk health_outcome exposure_dimension exposure_type exp rr bhd cutoff pop_fraction_type rr_conc impact_rounded
1 central central central central 0.1138961 3501.9619 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.369 30747 5 paf 1.128536 3502
1 central central lower central 0.1138961 3189.0894 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.369 28000 5 paf 1.128536 3189
1 central central upper central 0.1138961 3644.6736 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.369 32000 5 paf 1.128536 3645
1 lower central central central 0.0440064 1353.0658 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.124 30747 5 paf 1.046032 1353
1 lower central lower central 0.0440064 1232.1801 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.124 28000 5 paf 1.046032 1232
1 lower central upper central 0.0440064 1408.2058 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.124 32000 5 paf 1.046032 1408
1 upper central central central 0.1780300 5473.8882 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.664 30747 5 paf 1.216589 5474
1 upper central lower central 0.1780300 4984.8398 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.664 28000 5 paf 1.216589 4985
1 upper central upper central 0.1780300 5696.9598 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.664 32000 5 paf 1.216589 5697
1 central lower central central 0.0899213 2764.8092 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.369 30747 5 paf 1.098806 2765
1 central lower lower central 0.0899213 2517.7955 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.369 28000 5 paf 1.098806 2518
1 central lower upper central 0.0899213 2877.4806 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.369 32000 5 paf 1.098806 2877
1 lower lower central central 0.0344604 1059.5528 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.124 30747 5 paf 1.035690 1060
1 lower lower lower central 0.0344604 964.8902 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.124 28000 5 paf 1.035690 965
1 lower lower upper central 0.0344604 1102.7316 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.124 32000 5 paf 1.035690 1103
1 upper lower central central 0.1416706 4355.9450 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.664 30747 5 paf 1.165054 4356
1 upper lower lower central 0.1416706 3966.7760 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.664 28000 5 paf 1.165054 3967
1 upper lower upper central 0.1416706 4533.4583 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.664 32000 5 paf 1.165054 4533
1 central upper central central 0.1453304 4468.4726 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.369 30747 5 paf 1.170043 4468
1 central upper lower central 0.1453304 4069.2501 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.369 28000 5 paf 1.170043 4069
1 central upper upper central 0.1453304 4650.5716 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.369 32000 5 paf 1.170043 4651
1 lower upper central central 0.0567717 1745.5580 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.124 30747 5 paf 1.060189 1746
1 lower upper lower central 0.0567717 1589.6063 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.124 28000 5 paf 1.060189 1590
1 lower upper upper central 0.0567717 1816.6929 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.124 32000 5 paf 1.060189 1817
1 upper upper central central 0.2247829 6911.4001 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.664 30747 5 paf 1.289961 6911
1 upper upper lower central 0.2247829 6293.9214 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.664 28000 5 paf 1.289961 6294
1 upper upper upper central 0.2247829 7193.0531 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.664 32000 5 paf 1.289961 7193

Example: attribute with exposure categories (noise)

Goal: attribute cases of high annoyance (HA) to noise exposure

exdat_noise_ha <-
  exdat_noise_ha |>
  dplyr::filter(!is.na(exdat_noise_ha$exposure_mean))

results_noise_ha <- 
  healthiar::attribute_health(
    approach_risk = "absolute_risk",
    exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
    population = sum(exdat_noise_ha$population_exposed_total), # TO DO: hard code input here
    prop_pop_exp = exdat_noise_ha$population_exposed_total / 
      sum(exdat_noise_ha$population_exposed_total),
    erf_eq_central = "78.9270-3.1162*c+0.0342*c^2")

Source input data: NIPH
results_noise_ha[["health_detailed"]][["raw"]] |> knitr::kable()
geo_id_disaggregated erf_ci exp_ci population prop_pop_exp exposure_name approach_risk health_outcome exposure_dimension exposure_type exp erf_eq absolute_risk_as_percent pop_exp impact impact_rounded impact_per_100k_inhab
1 central central 945200 0.4099661 NA absolute_risk same_input_output 1 exposure_distribution 57.5 78.9270-3.1162c+0.0342c^2 12.81925 387500 49674.594 49675 5255.4585
1 central central 945200 0.3025815 NA absolute_risk same_input_output 2 exposure_distribution 62.5 78.9270-3.1162c+0.0342c^2 17.75825 286000 50788.595 50789 5373.3173
1 central central 945200 0.2029200 NA absolute_risk same_input_output 3 exposure_distribution 67.5 78.9270-3.1162c+0.0342c^2 24.40725 191800 46813.105 46813 4952.7196
1 central central 945200 0.0763860 NA absolute_risk same_input_output 4 exposure_distribution 72.5 78.9270-3.1162c+0.0342c^2 32.76625 72200 23657.232 23657 2502.8811
1 central central 945200 0.0081464 NA absolute_risk same_input_output 5 exposure_distribution 77.5 78.9270-3.1162c+0.0342c^2 42.83525 7700 3298.314 3298 348.9541

Iteration example (1/)

Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …

Tip

  • For iterations, enter geo unit-specific inputs as lists use as.list() function

  • Feed unique geo ID’s to the `geo_id_disaggregated` argument (e.g. municipality names)

  • Optional: aggregate geo unit-specific results by providing higher-level ID’s (e.g. region names)

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500))
)

Here the we want to aggregate results by language region ("Ger", "Fra", "Ita")

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = c(11, 11, 10, 8, 7) |> as.list(),
    bhd_central = c(4000, 2500, 3000, 1500, 500) |> as.list()
)

Let’s check the iteration output! (/)

Tip

The main output contains aggregated results if available, or disaggregated results if no aggregation ID was provided

results_iteration[["health_main"]]
geo_id_aggregated impact_rounded erf_ci exp_ci bhd_ci
Fra 466 central central central
Ger 1116 central central central
Ita 135 central central central

Iteration example with uncertainties

Analogously to the single geo unit example the iteration can also be run with uncertainties in one or more input variables.

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369, 
    rr_lower = 1.124,
    rr_upper = 1.664,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    exp_lower = as.list(c(10, 10, 9, 7, 6)),
    exp_upper = as.list(c(12, 12, 11, 9, 8)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500)),
    bhd_lower = as.list(c(3000, 1875, 2250, 1125, 375)),
    bhd_upper = as.list(c(5000, 3125, 3750, 1875, 625))
)
results_iteration[["health_detailed"]][["raw"]]
geo_id_aggregated geo_id_disaggregated erf_ci exp_ci bhd_ci cutoff_ci pop_fraction impact prop_pop_exp rr_increment erf_shape exposure_name approach_risk health_outcome exposure_dimension exposure_type exp rr bhd cutoff pop_fraction_type rr_conc impact_rounded
Ger Zurich central central central central 0.1717567 687.02680 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 4000 5 paf 1.207375 687
Ger Zurich central central lower central 0.1717567 515.27010 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 3000 5 paf 1.207375 515
Ger Zurich central central upper central 0.1717567 858.78350 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 5000 5 paf 1.207375 859
Ger Zurich lower central central central 0.0677332 270.93284 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 4000 5 paf 1.072654 271
Ger Zurich lower central lower central 0.0677332 203.19963 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 3000 5 paf 1.072654 203
Ger Zurich lower central upper central 0.0677332 338.66605 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 5000 5 paf 1.072654 339
Ger Zurich upper central central central 0.2632706 1053.08236 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 4000 5 paf 1.357350 1053
Ger Zurich upper central lower central 0.2632706 789.81177 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 3000 5 paf 1.357350 790
Ger Zurich upper central upper central 0.2632706 1316.35295 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 5000 5 paf 1.357350 1316
Ger Zurich central lower central central 0.1453304 581.32145 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 4000 5 paf 1.170043 581
Ger Zurich central lower lower central 0.1453304 435.99109 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 3000 5 paf 1.170043 436
Ger Zurich central lower upper central 0.1453304 726.65181 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 5000 5 paf 1.170043 727
Ger Zurich lower lower central central 0.0567717 227.08661 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 4000 5 paf 1.060189 227
Ger Zurich lower lower lower central 0.0567717 170.31496 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 3000 5 paf 1.060189 170
Ger Zurich lower lower upper central 0.0567717 283.85826 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 5000 5 paf 1.060189 284
Ger Zurich upper lower central central 0.2247829 899.13164 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 4000 5 paf 1.289961 899
Ger Zurich upper lower lower central 0.2247829 674.34873 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 3000 5 paf 1.289961 674
Ger Zurich upper lower upper central 0.2247829 1123.91454 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 5000 5 paf 1.289961 1124
Ger Zurich central upper central central 0.1973659 789.46375 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.369 4000 5 paf 1.245898 789
Ger Zurich central upper lower central 0.1973659 592.09781 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.369 3000 5 paf 1.245898 592
Ger Zurich central upper upper central 0.1973659 986.82969 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.369 5000 5 paf 1.245898 987
Ger Zurich lower upper central central 0.0785674 314.26953 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.124 4000 5 paf 1.085267 314
Ger Zurich lower upper lower central 0.0785674 235.70214 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.124 3000 5 paf 1.085267 236
Ger Zurich lower upper upper central 0.0785674 392.83691 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.124 5000 5 paf 1.085267 393
Ger Zurich upper upper central central 0.2998475 1199.38980 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.664 4000 5 paf 1.428260 1199
Ger Zurich upper upper lower central 0.2998475 899.54235 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.664 3000 5 paf 1.428260 900
Ger Zurich upper upper upper central 0.2998475 1499.23725 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.664 5000 5 paf 1.428260 1499
Ger Basel central central central central 0.1717567 429.39175 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 2500 5 paf 1.207375 429
Ger Basel central central lower central 0.1717567 322.04381 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 1875 5 paf 1.207375 322
Ger Basel central central upper central 0.1717567 536.73969 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 3125 5 paf 1.207375 537
Ger Basel lower central central central 0.0677332 169.33303 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 2500 5 paf 1.072654 169
Ger Basel lower central lower central 0.0677332 126.99977 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 1875 5 paf 1.072654 127
Ger Basel lower central upper central 0.0677332 211.66628 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 3125 5 paf 1.072654 212
Ger Basel upper central central central 0.2632706 658.17648 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 2500 5 paf 1.357350 658
Ger Basel upper central lower central 0.2632706 493.63236 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 1875 5 paf 1.357350 494
Ger Basel upper central upper central 0.2632706 822.72060 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 3125 5 paf 1.357350 823
Ger Basel central lower central central 0.1453304 363.32591 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 2500 5 paf 1.170043 363
Ger Basel central lower lower central 0.1453304 272.49443 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 1875 5 paf 1.170043 272
Ger Basel central lower upper central 0.1453304 454.15738 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 3125 5 paf 1.170043 454
Ger Basel lower lower central central 0.0567717 141.92913 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 2500 5 paf 1.060189 142
Ger Basel lower lower lower central 0.0567717 106.44685 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 1875 5 paf 1.060189 106
Ger Basel lower lower upper central 0.0567717 177.41141 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 3125 5 paf 1.060189 177
Ger Basel upper lower central central 0.2247829 561.95727 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 2500 5 paf 1.289961 562
Ger Basel upper lower lower central 0.2247829 421.46795 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 1875 5 paf 1.289961 421
Ger Basel upper lower upper central 0.2247829 702.44659 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 3125 5 paf 1.289961 702
Ger Basel central upper central central 0.1973659 493.41484 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.369 2500 5 paf 1.245898 493
Ger Basel central upper lower central 0.1973659 370.06113 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.369 1875 5 paf 1.245898 370
Ger Basel central upper upper central 0.1973659 616.76856 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.369 3125 5 paf 1.245898 617
Ger Basel lower upper central central 0.0785674 196.41845 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.124 2500 5 paf 1.085267 196
Ger Basel lower upper lower central 0.0785674 147.31384 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.124 1875 5 paf 1.085267 147
Ger Basel lower upper upper central 0.0785674 245.52307 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.124 3125 5 paf 1.085267 246
Ger Basel upper upper central central 0.2998475 749.61863 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.664 2500 5 paf 1.428260 750
Ger Basel upper upper lower central 0.2998475 562.21397 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.664 1875 5 paf 1.428260 562
Ger Basel upper upper upper central 0.2998475 937.02328 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 12 1.664 3125 5 paf 1.428260 937
Fra Geneva central central central central 0.1453304 435.99109 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 3000 5 paf 1.170043 436
Fra Geneva central central lower central 0.1453304 326.99331 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 2250 5 paf 1.170043 327
Fra Geneva central central upper central 0.1453304 544.98886 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.369 3750 5 paf 1.170043 545
Fra Geneva lower central central central 0.0567717 170.31496 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 3000 5 paf 1.060189 170
Fra Geneva lower central lower central 0.0567717 127.73622 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 2250 5 paf 1.060189 128
Fra Geneva lower central upper central 0.0567717 212.89370 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.124 3750 5 paf 1.060189 213
Fra Geneva upper central central central 0.2247829 674.34873 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 3000 5 paf 1.289961 674
Fra Geneva upper central lower central 0.2247829 505.76154 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 2250 5 paf 1.289961 506
Fra Geneva upper central upper central 0.2247829 842.93591 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10 1.664 3750 5 paf 1.289961 843
Fra Geneva central lower central central 0.1180609 354.18256 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.369 3000 5 paf 1.133865 354
Fra Geneva central lower lower central 0.1180609 265.63692 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.369 2250 5 paf 1.133865 266
Fra Geneva central lower upper central 0.1180609 442.72819 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.369 3750 5 paf 1.133865 443
Fra Geneva lower lower central central 0.0456812 137.04363 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.124 3000 5 paf 1.047868 137
Fra Geneva lower lower lower central 0.0456812 102.78272 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.124 2250 5 paf 1.047868 103
Fra Geneva lower lower upper central 0.0456812 171.30453 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.124 3750 5 paf 1.047868 171
Fra Geneva upper lower central central 0.1842846 552.85375 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.664 3000 5 paf 1.225918 553
Fra Geneva upper lower lower central 0.1842846 414.64031 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.664 2250 5 paf 1.225918 415
Fra Geneva upper lower upper central 0.1842846 691.06718 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.664 3750 5 paf 1.225918 691
Fra Geneva central upper central central 0.1717567 515.27010 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 3000 5 paf 1.207375 515
Fra Geneva central upper lower central 0.1717567 386.45258 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 2250 5 paf 1.207375 386
Fra Geneva central upper upper central 0.1717567 644.08763 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.369 3750 5 paf 1.207375 644
Fra Geneva lower upper central central 0.0677332 203.19963 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 3000 5 paf 1.072654 203
Fra Geneva lower upper lower central 0.0677332 152.39972 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 2250 5 paf 1.072654 152
Fra Geneva lower upper upper central 0.0677332 253.99954 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.124 3750 5 paf 1.072654 254
Fra Geneva upper upper central central 0.2632706 789.81177 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 3000 5 paf 1.357350 790
Fra Geneva upper upper lower central 0.2632706 592.35883 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 2250 5 paf 1.357350 592
Fra Geneva upper upper upper central 0.2632706 987.26471 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 11 1.664 3750 5 paf 1.357350 987
Ita Ticino central central central central 0.0899213 134.88190 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.369 1500 5 paf 1.098806 135
Ita Ticino central central lower central 0.0899213 101.16143 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.369 1125 5 paf 1.098806 101
Ita Ticino central central upper central 0.0899213 168.60238 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.369 1875 5 paf 1.098806 169
Ita Ticino lower central central central 0.0344604 51.69055 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.124 1500 5 paf 1.035690 52
Ita Ticino lower central lower central 0.0344604 38.76791 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.124 1125 5 paf 1.035690 39
Ita Ticino lower central upper central 0.0344604 64.61318 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.124 1875 5 paf 1.035690 65
Ita Ticino upper central central central 0.1416706 212.50586 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.664 1500 5 paf 1.165054 213
Ita Ticino upper central lower central 0.1416706 159.37939 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.664 1125 5 paf 1.165054 159
Ita Ticino upper central upper central 0.1416706 265.63232 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8 1.664 1875 5 paf 1.165054 266
Ita Ticino central lower central central 0.0608838 91.32577 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.369 1500 5 paf 1.064831 91
Ita Ticino central lower lower central 0.0608838 68.49433 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.369 1125 5 paf 1.064831 68
Ita Ticino central lower upper central 0.0608838 114.15721 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.369 1875 5 paf 1.064831 114
Ita Ticino lower lower central central 0.0231076 34.66138 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.124 1500 5 paf 1.023654 35
Ita Ticino lower lower lower central 0.0231076 25.99603 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.124 1125 5 paf 1.023654 26
Ita Ticino lower lower upper central 0.0231076 43.32672 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.124 1875 5 paf 1.023654 43
Ita Ticino upper lower central central 0.0968303 145.24552 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.664 1500 5 paf 1.107212 145
Ita Ticino upper lower lower central 0.0968303 108.93414 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.664 1125 5 paf 1.107212 109
Ita Ticino upper lower upper central 0.0968303 181.55690 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 7 1.664 1875 5 paf 1.107212 182
Ita Ticino central upper central central 0.1180609 177.09128 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 9 1.369 1500 5 paf 1.133865 177

Compare example

note: definitely include because WP5 will do

Additional analyses

Monetization

note: include definitely

Social analysis

note: if time allows

Other features

Check out the intro vignette for examples (to be added)

  • correlated exposures

  • life table analysis

  • get_daly

Exporting results

note: show how to export csv, …

Export to Excel

openxlsx::write.xlsx(results_pm_copd, file = "../testing/Workshops_and_demos/WS_WP5/exported_results/results_pm_copd.xlsx")

Exported to .xlsx format

Export to .csv

TODO

Visualize

Out of scope of healthiar

  • Using R, e.g. with ggplot2 package

  • Using Excel (once results are exported)

Introduce homework

note: stress that they must install & test healthiar before 2nd WS

note: AC: not more than 2-3 exercises, one with provided numbers, one where they use exdat

note: put exercises in PDF on Teams

If you encounter challenges during installation, get in touch with us!

Outlook WS2

note: mention programme WS2 & explicitly YOU WILL HAVE TO PROGRAMME IN RSTUDIO USING HEALTHIAR

note: document any suggestions for improvements